Multivariate Calculus

 


 

Calculus on Vector Functions:

All laws of calculus can be applied on vector functions. For scalar/vectors, see in "scalars and vectors" in Maths.

Vector func r(t) = f(t)i + g(t)j + h(t)k in 3D space, where i,j,k are x,y,z dir, and f(t), g(t), h(t) are component func which are real valued in terms of parameter "t". vector r(t) is also expressed as <f(t),g(t),h(t)>. Curve C is traced by the terminal point of vector r(t), and is known as the plot or graph of vector r(t). r(t) is written in bold or wih a arrow on top to indicare it's a vector.

Domain of a vector func is the intersection of domains of f, g and h. 

Representing any graph by a vector valued func: This can be done by choosing parameter "t", and defining component func in terms of t.

 ex: y = x^2 + 1. Choose x=t (may choose anything). y=t^2+1. So, vector r(t) = ti +(t^2+1)j. The direction of the curve is from t=-inf to t=+inf (left to right). IF we choose x=-t, then dir of curve would reverse, i.e from right to left. To sketch a curve given as parametrized func, it's usually eadier to convert it to c,y,z cartesian coordinates, and see if it coresponds to any of the known shapes. Otherwise, nly a computer program may sketch such curves, as they can get complicated.

Limit of Vector func: Limits follow same rule as sclar as each component i,j,k of vector func is a scalar func, so we just take limits of each scalar component func.

Continuity of vector func: It's cont at t=a, if each of it's cmponent func is cont at t=a.

Differentiation/Integration of Vector func:

Derivative: Dt([r(t)] = r'(t) = Lim Δt->0 r(t+Δt) - r(t) / Δt = f'(t)i + g'(t)j + h'(t)k => i.e take differentiation of each component of vector. Similarly higher order differentials may be found by successively differentiating.

Vector func r(t) is smooth on an open interval I, if f'(t), g'(t), h'(t) are cont on I, and r'(t) ≠ 0 (i.e derivative for all components are not 0) for any value of t in the interval I. There is no such requirement in scalar calculus, but shows up in vector calculus. Inline discussion suggests that having drivative as 0  reverses dir of the curve (te slope of curve doesn't keep going in same dir) which poduces cusps, resulting in non smooth func at those points. However, I couldn't find a formal proof for this. What's interesting is that it's not the curve traced by vector, that determines if it's differentiable, but the way the curve is traced, i.e by changing parameters, the same curve may now become undifferentiable.

Ex:  Take a plane parabola: (t, t^2, 0). According to the definition, it is smooth, and indeed, a parabola is a curve we would intuitively like to call smooth: At (0,0,0), differentiation is (1,2t,0)=(1,0,0), since it's not 0 for all components at origin, it's smooth => it doesn't take a sudden rough turn. However, consider the same parabola, but parametrize slightly differently: (t^3, t^6, 0). This is not smooth at t=0, as it's diff is (3t^2, 6t^5,0) = (0,0,0) at origin. The way the curve is traced made a difference. Below is one of the links explaiing this:

Source: https://www.physicsforums.com/threads/why-must-dr-dt-never-equal-zero-for-a-smooth-curve.180821/

Properties of derivatives of vector func: Same as in real func for given vector func r(t). All easy to prove by writing vector func in terms of components.

  • Dt([f(t)*r(t)] = f'(t)*r(t) + f(t).r'(t). Here f(t) is real func (as simple multiplication operation doesn't exist for 2 vectors).
  • Dt([r((f(t))] = r'(f(t))*f'(t). Here f(t) is real fun.
  • Dot product (of 2 vectors): Dt([u(t).r(t)] = u'(t)*r(t) + u(t).r'(t). This gives a scalar answer (as dot product is always a scalar)
  • Cross product (of 2 vectors): Dt([u(t) X r(t)] = u'(t) X r(t) + u(t) X r'(t). This gives a vector answer (as cross product is always a scalar)
  • Dot product: If r(t).r(t) = c (a constant), then r(t) . r'(t) = 0. This is a very important property, as it states that if r(t) itself is constant, then it's derivative, r'(t) will always be orthogonal to r(t). This is what is observed in Kinematics eqn in Physics. If velocity is constant, then it's derivative, acceleration vector is orthogonal to velocity (obvious as if acceleration was in dir of velocity, then velocity would have changed)

Integration: 

Integration is same as for scalar func, we integrate each component for both definite and indefinite integral. For indefinite integral, we have to add constant C for each component, so constantbecomes C1i + C2j + C3k = C vector. So, r(t)dt = R(t) +  C, where R'(t) = r(t) and both R(t) and C are vector func. If initial cond is given, then we can find vector C.

Displacement, velocity and acceleration:

From eqn above, displacement is curve C plotted by tip of r(t). The derivative of r(t) is velocity vector, v(t), and derivative of v(t) is acceleration vector, a(t). 

Vector r(t) can be from anywhere (not necessarily from origin), the curve plotted by r(t) remains same as C, as that's what the moving object is tracing.

 v(t) = r'(t) = x'(t)i + y'(t)k + z'(t)k. As Δt -> 0, v(t) becomes tangent to the curve C [as v(t) is pointing in same dir as r(t+Δt) - r(t) ]. 

a(t) = v'(t) = r''(t) = x''(t)i + y''(t)k + z''(t)k. (double differentiation).

We can also do reverse of diff, i.e integration to determine v(t) and r(t) from a(t) given some initial conditions to eliminate constant vector C.

Projectile motion: This is eqn of a particle thrown at an angle from height h1 and hitting a target at heohgt h2. The curve traced by this motion is a parabola (i.e quadratic eqn). 

a(t) = -gj, where g=9.8m/s^2 (or 32 ft/s^2), and t is time

v(t) = ∫ a(t)dt = -gtj + C1

r(t) = ∫ v(t)dt =  ∫ [-gtj + C1] dt = -gt^2/2j + C1t + C2

To find C1 and C2, you may use initial cond: v(0) = v0 and r(0) = r0. These are both vectors.

Solving, we get: r(t) = -gt^2/2j + v0t + r0, where v0 and r0 are both vectors

In most cases, initial height and speed along with angle of projection are given, which gives following:

r(0) = +h1j

v(0) = ucos(θ)i + usin(θ)j, where u is the initial speed, and theta is angle of projection measured from horizontal surface.

So,  r(t) = -gt^2/2j + v0t + r0, =  -gt^2/2j +  utcos(θ)i + utsin(θ)j + h1j =  [ucos(θ)]t i + [h1 + [usin(θ)]t -gt^2/2] j

v(t) = -gtj + [ucos(θ)]i + [usin(θ)]j =  [ucos(θ)] i + [usin(θ) - gt] j

From rqn above, we can very easily find all the stats w/o knowing any physics, just calculus info is enough:

  • Max height: Max height is achieved when velocity vector has no vertical component, i.e V(t1) = x i + 0j => u*sin(θ) - gt1 =0 => t1 = u*sin(θ)/g. Substitute t1 in r(t) to get max height.
  • Total time to travel: It's when j component of r(t) =0 =>  [h1 + [usin(θ)]t -gt^2/2] = 0 => gives time t. Ignre the value with -ve sign before the sq root.
  • Total distance traveled: It's when j component of r(t) =0 => We got time from above eqn, we subtitute in r(t) =  [ucos(θ)]t with t from above eqn.
  • If projectile motion hits object at height h2, then r(t1) = Di + h2j => h2 = [h1 + [usin(θ)]t -gt^2/2] => [(h1-h2) + [usin(θ)]t -gt^2/2] =0 => we get t here, with h1 replaced by (h1-h2).

 


 

Functions of more than one variable:

Chapter 13:

If we have a func like z=f(x,y)= x^2+y^2, it's a func of 2 var. We can graph this in 3D space with points (x,y,z), which will be a curved surface. The domain is all possible values of (x,y) that the func can take, and range is all possible values of z.

Level curve or contour lines are a 2D map of multiple lines where along each line, the value of f(x,y) is constant. Drawing these lines on 2D map gives an idea of how the 3D surface looks like. Similar to Level curve, we've level surface when we extend the func to 3 var, i.e f(x,y,z). It's a graph where f(x,y,z) = contant along those surfaces (this shows in a 3D plot).

ex: z=f(x,y) = √ (16 - 4*x^2 - y^2) 

  • Domain: Here the eqn under sq root ≥ 0 for it to be real number. Hence domain is   (16 - 4*x^2 - y^2) ≥ 0. The boundary of this is an ellipse x^2/4 + y^2/16 = 1. All points on or inside this ellipse are in the domain, i.e x^2/4 + y^2/16 ≤ 1. 
  • Range: Range of this func is all possible z values. Smallest z value is 0, which happens on the boundary of the ellipse. The eqn under sq root can't be > 16, as the smallest possible value of x^2 and y^2 is 0.  So, range of z is 0  ≤ z  ≤ 4.
  • To plot the graph of (x,y,z), we get z^2 = 16 - 4*x^2 - y^2 => x^2/4 + y^2/16 + z^16 = 1, with 0  ≤ z  ≤ 4 which is eqn of ellipsoid with only upper half included due to militaion of z being +ve number only.

 

Limits and Continuity:

Limits for 2 var func follows same principle as that for 1 var, For limit to exist for 1 var func, f(x) at x=a, we calc value of f(x) as x approaches "a" from left and right, and if they are both equal to L, then limit exists and is equal to L. In func of 2 var, f(x,y), for limit to exist at f(x0,y0), the value of func should equal to L from all directions, not just from 2 axis. 

Formal defn:

 Lim (x->x0, y->y0) f(x,y) = L if for each ε > 0, there exists a δ > 0, st |f(x,y) - L| <  ε whenever 0 < R <  δ, where R = √ [ (x-x0)^2 + (y-y0)^2] (i.e R is the radius of the open circle centered at (x0,y0)

Graphically, this implies that for any point (x,y) ≠ (x0,y0)  in the disk of radius δ, the value of f(x,y) lies b/w (L - ε)  and (L + ε). 

It's sometimes not easy to prove whether a limit in func of multiple var, exists or not. We may get limit by directly substituting where undefined values as 0/0 or ∞/∞ doesn't show up. In some cases, we may show that limit doesn't exist by approaching along x-axis, and then along y-axis or may be along y=x, and show that it results in diff values.

Continuity: A func of 2 var f(x,y) is continuous at pt (x0,y0) in a open region R if Lim (x->x0, y->y0) f(x,y) = f(x0,y0). To be cont in whole region R, func needs to be cont at all points in R.

Removable discontinuity: Some func have a limit eist at a point, but the value doesn't exist at that particular pt. For such func, we can make them cont, by defining the value of that func at that pt to be equal to the limit. Sunc discontinuity is called Removable discontinuity. For some func that discontinuity can't be removed, and is called non-removable discontinuity.

If 2 func f and g are cont at (x0,y0), then below are also cont:

  • Scalar multiple: kf
  • sum/diff: f+g, f-g
  • Product: f*g
  • quotient f/g provided g(x0,y0) ≠ 0

Composite func: If func h is cont at (x0,y0), and g is cont at h(x0,y0), then composite func g o h = g(h(x,y) is cont at (x0,y0), i.e Lim (x->x0, y->y0) g(h(x,y)) = g(h(x0,y0)). NOTE: here h is a func of 2 var, while g is a func of 1 var.

The limit and continuity defn can be extended to 3 var func, i.e f(x,y,z) in the same way s 2 var func.

Partial derivatives: When we differentiate a func of mult var wrt only 1 var, treating all other var as constant, we call it partial derivatives and are defined as below, provided limit exists. 

1st partial derivative wrt x is fx = ∂f/∂x = Lim Δx->0   [f(x+Δx,y) -  f(x,y)] / Δx => This denotes the slope of the curve in x direction.

1st partial derivative wrt y is fy = ∂f/∂y = Lim Δy->0   [f(x, y+Δy) -  f(x,y)] / Δy => This denotes the slope of the curve in y direction.

Partial derivative of func of n var can be defined in similar way as above => differentiate wrt one var, and hold all other var as constant.

Higher order Partial derivatives: We can find 2nd, 3rd and so on partial derivatives provided such derivatives exist. It's defined depending on the order in which partial differentiations are done.

2nd order partial derivative wrt x is fxx = ∂/∂x (∂f/∂x) = ∂2f/∂x2 . Similarly 2nd order partial derivative wrt y is fyy = ∂/∂y (∂f/∂y) = ∂2f/∂y2 

Mixed partial derivatives: 

Diff 1st wrt x and then wrt y is fxy = ∂/∂y (∂f/∂x) = ∂2f/∂y∂x.

Reversing the order, we can Diff 1st wrt y and then wrt x is fyx = ∂/∂x (∂f/∂y) = ∂2f/∂x∂y.

Thm: if both  fxy and  fyx are cont in an open disc R, then for every (x,y) in R,  fxy =  fyx. Similarly for third and higher order mixed partial derivatives, the order of diff doesn't matter, as long they are all differntiated wrt same var. i.e  fxzz =    fzxz =   fzzx 

Increments and Differentials: 

For func of 2 var z=f(x,y), then increment Δz = f(x+ Δx,y+ Δy) - f(x,y) (similar to the one for single var, where dy = f'(x)dx)

Writing diff dx=Δx, dy=Δy, dz=Δz, total diff of z is dz = (∂z/∂x)*dx + (∂z/∂y)*dy =  fx(x,y)dx + fy(x,y)dy

For 4 var, w=f(x,y,z,u), dw = (∂f/∂x)*dx + (∂f/∂y)*dy + (∂f/∂z)*dz + (∂f/∂u)*du =  fx(x,y,z,u)dx + fy(x,y, z, u)dy + fz(x,y,z,u)dz + fu(x,y, z, u)du. 

Differentialiability Thm: Func f(x,y) is diff at (x0,y0) if it can be written in the form Δz =  fx(x0,y0)Δx + fy(x0,y0)Δy +  ε1*Δx +  ε2*Δy where  ε1, ε2 -> 0 as Δx,Δy -> 0. Func is diff in region R if it's diff at every pt in R

NOTE sufficent cond for diff: Above req is diff than that for 1 var func. For 1 var func, the func is diff at that pt, if derivative exists. For 2 var func, just the existence of partial derivatives ( fx , fy) at that pt is not enough to guarantee diff. Both partial derivatives ( fx , fy) will need to be cont too at that pt for the func to be diff. Corollary is always true => i.e if a func is diff at (x0,y0), then it's cont at (x0,y0).

For whole region R, partial derivatives need to be cont at all points in R for the func to be diff in R.

Linear Approximation: Similar to 1 var to approximate the func in vicinity of a pt (x0,y0) =>  dz = (∂z/∂x)*dx + (∂z/∂y)*dy. Here dx=Δx, dy=Δy, dz=Δz

Chain Rule: Chain rule is similar to the one for single var. It can be extended to more than 2 var too. 2 cases:

  1. One indep var "t": w=f(x,y) where x and y are defined as func of one other independent var "t", x=g(t), y=h(t), where g and h are diff func of "t, then w is a diff func of t, given by
    1. dw/dt = (∂w/∂x)*dx/dt + (∂w/∂y)*dy/dt 
  2. Two indep var "s,t": w=f(x,y) where x and y are defined as func of 2 other independent var "s,t", x=g(s,t), y=h(s,t). If all partial derivatives exist, then partial derivatives of w wrt s,t, is given by
    1. (∂w/∂s) = (∂w/∂x)*(∂x/∂s) + (∂w/∂y)*(∂y/∂s)
    2. (∂w/∂t) = (∂w/∂x)*(∂x/∂t) + (∂w/∂y)*(∂y/∂t)

Implicit partial differentiation (PD): Chain rule can be used to find PD of func defined implicitly, in a much easier way, than trying to find derivative directly. Consider a func F(x,y)=0, where it's assumed that y=f(x). We could dy/dx by differentiating f(x), which may or may not be possible. There in another neat way:

Let w = F(x,y) = F(x,f(x)) = 0=> dw = Fx(x,y)dx + Fy(x,y)dy, also dw/dx = 0 (since diff of const=0 is 0). => dw/dx = Fx(x,y) + Fy(x,y)dy/dx = 0 => dy/dx = -Fx(x,y)/Fy(x,y)

NOTE: Always remember that when writing func as F(x,y), y is not an indep var. It's a func of x, i.e when x changes, y also changes accordingly based on the eqn. If y were indep, then dy/dx would be equal to 0, since changing x would have no relation to y. Even though we didn't assume y to be dependent on x in above proof, it worked out since 

Similarly for func of 3 var, F(x,y,z)=0, and assuming z=f(x,y) and if we want to find PD ∂z/∂x and ∂z/∂y, then we can use multiple methods. Using normal differentiation,  ∂z/∂x = fx(x,y), and  ∂z/∂y = fy(x,y). However, if we don't have a way to find f(x,y), then we can employ same technique as above. Note that z is not indep var, but dependent on (x,y). Define w=F(x,y,z)=0 => dw/dx = Fx(x,y,z) + Fy(x,y,z)dy/dx + Fz(x,y,z)dz/dx = 0 => Now since x,y are indep var, dy/dx=0 => Fx(x,y,z) +  Fz(x,y,z)dz/dx = 0 => dz/dx = -Fx(x,y,z) / Fz(x,y,z). Since y is indep var, so dz/dx = ∂z/∂x, hence ∂z/∂x =  -Fx(x,y,z) / Fz(x,y,z).

Similarly  ∂z/∂y =  -Fy(x,y,z) / Fz(x,y,z).

Directional derivative:

Let z=f(x,y) be a func, and "u" be a unit vector in xy plane, given by u=(cos Θ)i + (sin  Θ)j, where Θ is the angle the vector makes with +ve x-axis.We saw that we can find slope of curve in x dir and y dir by taking PD  fx and fy. To find slope in dir parallel to u vector (in dir Θ), it can be proved that slope is  fx*(cos Θ) +fy*(sin  Θ). 

This is called directional derivative of f(x,y) in dir of unit vector u, denoted by Duf(x,y) =  fx(x,y)*(cos Θ) +fy(x,y)*(sin  Θ). 

NOTE: u is unit vector. If another non unit vector is given, then you need to convert it to unit vector to get on form of (cos Θ)i + (sin  Θ)j. Also, before you find dir derivative, you need to prove that PD are continuous and hence differentiable (just existence of PD doesn't prove they are differentiable). 

Gradient of func: Gradient is a vector and is defined as grad f(x,y) =  ∇f(x,y) =  fx(x,y)i +fy(x,y)j , where i and j are components along x and y axis. Note that this vector is in x-y plane and not in 3D space.

Directional derivative is dot product of grad of Func and the unit vector.

Duf(x,y) = ∇f(x,y) . u (easy to prove, as ∇f(x,y) =  fx(x,y)i +fy(x,y)j, and u=(cos Θ)i + (sin  Θ)j, doing dot product gives the result)

Dir of max/min ascent/descent: There are many directional derivatives at a given point in a curve (since angle can go from 0 to 360). Most of the times, we are interested in finding the dir where f(x,y) inc most rapidly (i.e the dir of steepest ascent) or  the dir where f(x,y) dec most rapidly (i.e the dir of steepest descent). We are trying to find max/min value of Duf(x,y), which happens in the dir of ∇f(x,y) 

Thm:

  1. If ∇f(x,y) = 0, then Duf(x,y) = 0 for all u.
  2. Dir of max inc of f is given by  +∇f(x,y). The max value of  Duf(x,y) = + || ∇f(x,y) ||
  3. Dir of min inc of f is given by  -∇f(x,y). The min value of Duf(x,y) =  - || ∇f(x,y) ||
  4. If f(x,y) is diff at (x0,y0) and ∇f(x0,y0) ≠ 0 , then ∇f(x0,y0) is normal to the level curve thru (x0,y0). => The rate of change in the dir of unit tangent vector is 0.
  5. All above thm can be expanded to func of 3 var and more.

Tangent plane and normal lines to a surface:

Let's say z=f(x,y), which reps a surface S. We can write this as F(x,y,z) = f(x,y) - z = 0, which reps he same same surface S, but now it's a level Surface with constant value = 0. For any surface, we can find a normal line to the surface, as well as a tangent plane to the surface. 

For a pt P (x0,y0,z0) on Surface S, given by eqn F(x,y,z)=0, let's find tangent line/surface and normal line.

Tangent and Notmal Lines on the surface: Let C be a curve on S passing thru pt P. We can write it's eqn as vector r(t) = x(t)i + y(t)j + z(t)k , then for all t, F(x(t),y(t),z(t))=0 => dF/dt = Fx(x,y,z).dx/dt + Fy(x,y,z).dy/dt + Fz(x,y,z).dz/dt = 0 (by diff both sides) => ∇F(x,y,z) . r'(t) = 0 (dot product of 2 vectors) => ∇F(x,y,z) and r'(t) are orthogonal to each other. At pt P (x0,y0,z0), ∇F(x0,y0,z0) . r'(t0) = 0. Since r'(t0) is a tangent vector, it implies that  ∇F(x0,y0,z0) is a normal vector perpendicular to all the the tangent lines at S.

Line thru P having dir of ∇F(x,y,z) is called the normal line to S at P. Parametric eqn for this normal line is (x-x0)/a = (y-y0)/b = (z-z0)/c , where a,b.c are the direction numbers (coeff of i, j, k) in ∇F.

Tangent plane: The plane thru P that is normal to ∇F(x,y,z) is called the tangent plane to S at pt P. To find eqn of this plane, consider vector v = (x-x0)i + (y-y0)k + (z-z0)k, where (x,y,z) is any pt that lies on this plane. Then ∇F(x0,y0,z0) . v = 0 (dot product of 2 vectors is 0 as v is orthogonal to ∇F) => Eqn of plane is  Fx(x0,y0,z0)*(x-x0) + Fy(x0,y0,z0)*(x-x0) + Fz(x0,y0,z0)*(x-x0) = 0

 


 

scalars and vectors:

This topic is covered in Physics as well as in maths (in calculus). I'm covering in Physics as most of it's application is in Physics.

Scalars: Things which don't have any sense of direction, are called scalars. As an example of scalar, mass of a thing, is a scalar quantity. It doesn't have any sense of direction, whether it's going north, south, etc, the mass is the same. Similarly for time, temp, volume, etc.

Vectors: Things which have direction along with a magnitude, are called vectors. As an example of vector, force is a vector quantity. It depends on which direction you apply force, so it has a sense of direction. If the force is applied in same direction as the movement of the body, then the body will accelerate, while if it's applied in opposite dir, then the body slows down.So, direction of force matters. Similarly for velocity, acceleration, etc.

Link => https://math.libretexts.org/Bookshelves/Calculus/Supplemental_Modules_(Calculus)/Vector_Calculus/1%3A_Vector_Basics

Vector: A vector is rep by an arrow, whose length is the magnitude of the vector, and whose dir is the angle that that vector makes wrt x and y axis (for 2D vector), or wrt x, y and z axis (for 3D vector). Vectors can be moved anywhere in space and will remain same, as long as mag and dir remain same (i.e starting point and ending pt of vector can be anywhere).

Vector is rep by i, j, k for x,y and z dir or by coords <x,y,z>, (x,y,z) or in matrix form as x, y, z written vertically in 1st column. An arrow is put on top of the line segment to indicate it's vector. Magnitude or norm of vector is denoted by ||PQ||, while dirn is denoted by an angle (tan(θ)=slope). If ||v||=1, or v/||v|| it's called a unit vector. i, j, k are called std unit vectors in dir of x, y and z axis. 

For simplicity, initial point of vector is usually chosen as origin, so only the end pt need to be specified in such a case. This rep of vector is called std position. or component form of vector. End point Coords <v1,v2> are called components of vector v. 

Direction cosine: The 3 angles (α, β, γ) that a vector makes with x, y and z axis are called direction angles of v, and the 3 cosine values are called Direction cosine. For v=<v1,v2,v3>, v1=||v||*cos(α),  v2=||v||*cos(β),  v3=||v||*cos(γ). This yields cos2(α) +  cos2(β)  + cos2(γ) = 1 

NOTE: differentiation/integration of vectors is discussed in Calculus section.

 We'll study some operations (algebra) on vector quantities.

  • Addition/subtraction: Addition/subtraction of vectors is done by placing end point of a vector to the start point of next vector and so on, until all vectors are connected. Then you connect the start point of 1st vector to end point of last vector, and that's the sum of all the vectors. For subtraction, you still treat it as addition, except that the direction of vector is reversed for the subtracting vectors. You still place them the same way as in addition, except that -ve vector is pointing in other dir.
    • For vector <x1,y1> and <x2,y2>, the sum is <x1+x2,y1+y2>. The magnitude of vector is sq rt of [(x1+x2)^2 + (y1+y2)^2]. Mag may also be calc by using cosine thm, , if you know the mag of 2 vectors and angle b/w them.
    • Many properties as Commutative, associative, identity, distributive properties hold true for vector and can be proved easily by using above formula. Any set of vectors that satisfy these 8 properties is called "vector space"
  • Multiplication: There's no multiplication of vectors. We call it the product of vectors. Product of vectors may give a scalar or a vector depending on how we define it. There are 2 kinds of product. Link => https://math.libretexts.org/Bookshelves/Calculus/Supplemental_Modules_(Calculus)/Vector_Calculus/1%3A_Vector_Basics/1.5%3A_The_Dot_and_Cross_Product 
    • Dot product (scalar product): It's a scalar, and is determined by multiplying coefficients as shown in link above.
      • Dot product is simply multiplying the length of one vector, with the length of the shadow (or projection) of the other vector on the 1st vector. So, u.v = ||u||.||v cos θ||  =  ||u||.||v|| cos θ. It can also be calculated by using formula u.v=u1.v1 + u2.v2 + u3.v3.  
        • Cosine Thm: By using cosine thm, we may write  ||u - v||^2 = ||u||^2 + ||v||^2 - 2 ||u||.||v||. cos θ => (u-v).(u-v) =   ||u||^2 + ||v||^2 - 2 ||u||.||v||. cos θ . Cancelling terms, we get u.v = ||u||*||v|| * cos(θ). This is how we get this formula for u.v. This implies that dot product of orthogonal vectors is 0. 
        • Projection of vectors: Many times, we decompose a vector into orthogonal components, once pointing towards X axis and other towards Y axis, which is called the projection of vector on x and y axis. These are the i,j coeff of vector. Some times, we want to find component of a vector in the dir of another vector (not necessarily in dir of i,j), then we need to find projection onto that vector.
          • Proj of u vector in dir of v vector is given by w1 = projv(u) = (u.v)/[||v||^2] * v. This is easy to prove, as dot product gives the product of projected vector component with the other vector. But then we need to divide by ||v|| since it includes that in dot product. So, (u.v)/||v|| gives the mag of such projection. We need to put dir in it (in dir of v vector), so we take unit vector in that dir, which is v/||v||. That gives above formula. ||u||cos(θ) is the component of u in dir of v. The component perpendicular to w1 is w2=u-w1.
      • Angle θ b/w the 2 vectors is taken to be b/w 0 to π. So, cos θ value goes from +1 to -1. Since ||u|| and ||v|| are +ve, u.v and cos θ will have same sign.
      • Dot product has many of the same properties as  Commutative, distributive properties and can be proved easily by using above formula.
    • Cross product (vector product): It's a vector, and is determined by multiplying coefficients as shown in link above. It also has dir (as the product is a vector) which is orthogonal to both the vectors whose cross product is being determined.
      • Cross product is found by getting determinant of the matrix with alternating +ve and -ve sign. It also has dir which is perpendicular to the plane of the 2 vectors u, v (i.e u X v is perpendicular to both u and v). 
        • Mag of u X v = ||u X v|| = |u|| * ||v|| * Sin(θ) (Note dot product was similar except that dot product had cos(θ)). This can be proved by using dot product. 
          • |u|| * ||v|| * Sin(θ) = |u|| * ||v|| * √ (1-Cos2(θ)) =  |u|| * ||v|| * √ (1 - [u.v/(||u||*||v||)]^2) =  √ (||u||^2*||v||^2 - [u1v1 + u2v2 + u3v3]^2) =   √ [(u1^2 + u2^2 + u3^2)*(v1^2+v2^2+v3^2) -  (u1v1 + u2v2 + u3v3)^2] which gives the reqd answer
          • ||u X v|| gives area of parallelogram with sides u and v. Easy to see as ||v|| sin(θ) is the height of the parallelogram.
        • u X v forma a right handed system.  curl your fingers in dir from u to v, then the thumb points in dir of u X v. k = i X j.
      • Cross product has properties as distributive and few others.
        • u X v = -(v  X u). 
        • u X (v+w) =  u X v + u X w.  
        • Triple scalar product: u.(v X w) = v.(w X u) = w.(u X v) => dot here reps the dot product
          • Triple Scalar product gives the vol of parallelopiped (a polyhedron, whose all sides are parallelogram) only if u, v, w don't lie in same plane. 
          • 3 vectors u, v, w which have same starting pt are coplanar if and only if their triple scalar product is 0.
        • u X v =0 if u and v are scalar multiples of each other (i.e u and v have same dir, i.e parallel to each other)
    • Scalar multiplication of vector v by a constant c, yields cv, i. mag of new vector is multiplied by c, with no change in dir (for c=-ve, dir is reversed). Parallel vectors are such that u=cv, where c is a scalar and u.v are 2 vectors. 

 

 Matrix: Matrices are useful when evaluating dot and cross product of matrices. Matrices are studied in Maths and provided in Advanced Maths section

 


 

3D geometry as vectors:

3D figures in space can be expressed as vectors, which sometimes yields eaiser way to manipulate them.

Lines in space:  A line in space can be rep as vector. Consider a line "L" which is parallel to vector v=<a,b,c>, and passes thru pt P (x1,y1,z1). Vector v is dir vector for line L, and a,b,c are dir numbers. We are looking for eqn of vector PQ, where Q is any pt <x,y,z>. Since PQ is parallel to v, we get PQ=t.v, where t is a scaling factor. Vec PQ =  (x-x1)i + (y-y1)j + (z-z1)k = t(ai + bj = ck) => x-x1=ta, y-y1=tb, z-z1=tc. This is called parametric eqn where "t" is an independent parameter which can take any real value, x=x1+at, y=y1+bt, z=z1+ct. This parametric eqn can also be written as symmetric eqn by eliminating t => (x-x1)/a = (y-y1)/b = (z-z1)/c

NOTE: neither parametric eqn nor  symmetric eqn are unique. By choosing some t, we can get other point on that line, and use that pt to get parametric and symmetric eqn which would be different. 

Planes in space:  A plane in space can be rep as an eqn by having a pt that lies on that plane, and having vector "n" which is perpendicular to that plane. Let's consider pt (x1,y1,z1) which lies on a plane and a normal vector n=<a,b,c>. Then plane consists of all points Q=(x,y,z) such that vec PQ is perpendicular to n. i.e PQ.n=0 => [(x-x1)i + (y-y1)j + (z-z1)k].<a.b.c>=0 => (x-x1)a + (y-y1)b +(z-z1)c = 0 => This is called the std form of plane. We can rearrange it to write general form of plane which is ax + by +cz + d =0. Given the general form of plane, it's easy to find a vector normal to plane as it's coeff a,b,c, so n=<a,b,c>

Find eqn of plane given 3 points in plane: First we form 2 vec from the 3 points, and find their cross product to get a vector normal to plane. Then we use above eqn to plug in a.b.c and then find d by using any of the points.

Find angle b/w 2 planes:  The angle b/w 2 planes can be found by finding normal vector of the 2 planes, and then finding angle b/w the 2 normal vectors using dot product.

Finding line of intersecton of 2 planes: Given 2 planes, they may either be parallel or intersect in a line. A line will only involve 2 var (x,y or y,z or x,z), so solve the 2 simultaneous eqn for 2 planes to eliminate a var, and we get the eqn for line intersecting them. We can also write it as parametric eqn by introducig var "t". Other way to get line of intersection, is to get normal vec n1=<a1,b1,c1> and n2=<a2,b2,c2> and then find cross product n1 X n2, The vec obtained is perpendicular to both vec, and hence has to be a intersecting line, since it's common to both of the planes. 

Trace of the plane: Plane in space intersects the coord plane (xy, yz, xz planes). The line of intersection is called the trace of the given plane in the coord plane. To find the xy trace, we set z=0, and so on for other planes. Any eqn of a straight line in 2 var is also a plane in 3D space, as it extends indefinitely in the plane of missing var (i.e 2x+y=5 is a line in 2D space, but a plane in 3D space). 

  • If 1 var is missing in eqn of plane, then it's parallel to the x axis. i.e 2x+y=5 is a plane parallel to the z axis.
  • If 2 var are missing in eqn of plane, then it's parallel to the coord plane. ex: x=2 is a plane parallel to y-z coord plane

Distance b/w point and plane: To find the distance of a plane to a pt Q, we take a arbitrary pt P (choose x=0, y=0 to get a simple pt z) on plane, and form a vector PQ, then project this vector onto normal vector. So D = ||projn(PQ)|| = (PQ.n)/||n||. The choice of pt P on plane doesn't change the answer.

  • For plane ax+by+cz+d=0, pt P on plane <x1,y1,z1> and pt Q = (x0,y0,z0), then D = PQ.n/||n|| = [(x0-x1)i + (y0-y1)j + (z0-z1)k].<a,b,c> / ||n|| = a(x0-x1) + b(y0-y1) + c(z0-z1) / √ (a^2+b^2+c^2) = | a*x0+b*y0+c*z0+d | / √ (a^2+b^2+c^2) ( we use the fact that a*x1+b*y1+c*z1+d=0, since pt (x1,y1,z1) lies on plane)

Distance b/w 2 parallel planes: To find the distance b/w 2 parallel planes, use a pt (x0,y0,z0) on 1 plane and then use the normal vector of other plane to find distance as shown above. So, formula is same as above

Distance b/w point and a line in space: To find the distance b/w pt and a line, we just do cross product instead of dot product. So, D= ||PQ X u|| / ||u||, where P is the pt o line, and Q is the other point, and u is direction vector of the line. Proof is simple PQ*Sin(θ) is the distance, which is what the cross product gives.

Spheres in space: The eqn for sphere is just an extension of circles in 2D, (x-x0)^2 + (y-y0)^2+ (z-z0)^2 = r^2 

Cylinders in space: Cylinders are any general curve, which is extended by copying and placing that curve on top of each other indefinitely in the 3D space. Such a curve is called the generating curve, since the 3D figure is obtained by repeating this curve by parallel lines, which are called rulings.

Simplest ex of cylinder is a cylinder obtained by repeating a circle: x^2+y^2=r^2. This is a circle in 2D, but in3D it becomes a cyclinder since z can take on any value.

Other ex: z = y^2, Z=sin(x), etc

Quadric Surfaces: These are 3D analog of conic sections. It's 2nd degree eqn of form ax^2 + by^2 + cz^2 + Dxy + Exz + Fyz + Gx + Hy + Iz + J = 0

There are 6 types of Quadric surfaces:

  1. Ellipsoid: 3D extension of ellipse in 2D: x^2/a^2 + y^2/b^2 + z^2/c^2 = 1. The surface is a sphere if a=b=c≠0.
  2. Hyperboloid: Here all var are raised to 2nd power, and one or two of the var have -ve coeff. 
    1. Hyperboloid of 1 sheet (only one coeff is -ve): x^2/a^2 + y^2/b^2 - z^2/c^2 = 1. It's hyperbola rotated around one of the axis. The axis of the Hyperboloid corresponds to the var whose coeff is -ve (in his case, that axis is "z" axis)
    2. Hyperboloid of 2 sheets (two coeff are -ve): x^2/a^2 - y^2/b^2 - z^2/c^2 = 1. It's hyperbola rotated and separated in 2 parts. The axis of the Hyperboloid corresponds to the var whose coeff is +ve (in his case, that axis is "x" axis)
  3. Elliptic cone: It's a cone shape opening in top and bottom. Eqn is x^2/a^2 + y^2/b^2 - z^2/c^2 = 0 (NOT 1 as in Ellipsoid).  The axis of the cone corresponds to the var whose coeff is -ve (in his case, that axis is "z" axis)
  4. Paraboloid: One of the var is raised to only 1st power.
    1. Elliptic paraboloid: It's a parabola in shape of ellipse (similar to a cone pointing upwards, but with gradual edges, i.e parabola rotated around). Eqn is z= x^2/a^2 + y^2/b^2. The axis of the paraboloid corresponds to the var raised to 1st power (in his case, that axis is "z" axis)
    2. Hyperbolic paraboloid: It's a parabola in shape of hyperbola. Eqn is z= y^2/b^2 - x^2/a^2(NOTE the -ve sign). The axis of the paraboloid corresponds to the var raised to 1st power (in his case, that axis is "z" axis)

 NOTE: some 2nd degree eqn in x,y,z don't represent any quadric surface. Ex: ^2=y^2+z^2 is a single point (0,0,0). x^2+y^2=1 is a right circular cylinder.

The general eqn ax^2 + by^2 + cz^2 + Dxy + Exz + Fyz + Gx + Hy + Iz + J = 0 shows a quadric surface not centered at origin. 

Surface of revolution: This is 5th special kind of surface that employs calculus to find surface area, vol, etc. 

  • Consider eqn y=r(z) in yz plane.
    • If we revolve this around z axis, we'll get a surface of revolution around z axis. The trace will be a circle, so x^2+y^2=[r(z)]^2.
    • If we revolve this around y axis, we'll get a surface of revolution around z axis. The trace will be a circle, so x^2+z^2=[r(y)]^2.
    • If we revolve this around x axis, we'll get a surface of revolution around z axis. The trace will be a circle, so y^2+z^2=[r(x)]^2.

NOTE: The generating curve for a surface of revolution is not unique.

 


 

Cylindrical and spherical coordinates:

Representing coords in (x,y,z) in rectangular coord is how we represent surfaces in space. However, sometimes, some other coord system are easier to represent some particular surfaces.

Cylindrical Coord => This is an extension of polar coord in a plane to 3D space. It's rep by (r, θ, z), where (r, θ) is the polar rep of projection of pt P onto planar xy plane, and z is the distance from  (r, θ) to P. Since rep in polar coord is not unique, so rep in cylindrical coord is also not unique.

  • Cylindrical to rectangular coord: x=r*Cos(θ),  y=r*Sin(θ), z=z
  • Rectangular to Cylindrical coord: r= √ (x^2+y^2), tan(θ) =y/x, z=z
  • Vertical plane rep: θ=c, Horizontal plane rep: z=c

Spherical Coord => This is rep by (ρ, θ, φ), where ρ = distance of pt P from origin, θ is the same angle used in cyclindrical coord (ngle formed by projectionn of Pt P onto xy plane), and φ is the angle b/w +ve z axis and line from origin to point P.  So, spherical coords are what is used to rep points on earth, where θ=longitude and φ=latitude. Sphericl coord is useful for surfaces that have a point or center of symmetry.

ρ and  φ are non -ve (i.e always +ve)

  • Spherical to rectangular coord: x=ρSin(φ)Cos(θ), y=ρSin(φ)Sin(θ), z=ρCos(φ)
  • Rectangular to Spherical coord: ρ= √(x^2+y^2+z^2), tan(θ) =y/x, Cos(φ)=z/√(x^2+y^2+z^2)
  • Spherical to Cylindrical coord (r ≥ 0): r^2=ρ^2Sin2(φ), θ=θ, z=ρCos(φ)
  • Cylindrical to Spherical coord (r ≥ 0): ρ= √(r^2+z^2), θ=θ, Cos(φ)=z/√(r^2+z^2)

 


 

Chemical Reactions:

Khan Academy => https://www.khanacademy.org/science/ap-chemistry-beta/x2eef969c74e0d802:chemical-reactions

Chemical reactions cause chemical changes, while non-chemical reactions cause only physical change. Ex: ice melting to water is physical change, while propane burning is a chemical change as it results in formation of new compounds. Chemical change results in making and breaking of bonds.

Consider the reaction between NaCl (sodium Chloride) and AgNO3 (silver Nitrate). They are both solids, but they can be dissolved in a solution (usually water), and then they are denoted by "aq" to indicate they are in aqueous solution. This changes how the 2 compounds react.

Salt (NaCl) dissolves in water. We think of crystalline salt as solid NaCl, and these indeed exist as molecule of NaCl. But when dissolved n water, Na and Cl actually dissociate to form Na+ ions and Cl- ions, surrounded by water molecules, with Na+ surrounded by partially -ve Oxygen atoms of water, while Cl- surrounded by partially +ve Hydrogen atoms of water. Similarly Silver Nitrate dissociates into Ag+ and NO3-.

We write eq as follows in Molecular Eq as follows: NaCl (aq) + AgNO3 (aq) => NaNO3 (aq) + AgCl (s) [NOTE: AgCl solid is formed from the above reaction, as it precipitates out of the solution, it doesn't have high solubility]

To better represent that the aq solution actually consists of dissociated ions, we can write eq in complete Ionic Eq as follows:  Na+ (aq) + Cl- (aq) +  Ag+ (aq) + NO3- (aq) => Na+ (aq)  + NO3- (aq) + AgCl (s) 

We can write above eqn in Net ionic eqn as follows: Cl- (aq) +  Ag+ (aq) => AgCl (s) [By cancelling out spectator ions which are the ions which are just watching the reaction, In this case Na+ (aq) and NO3- (aq) are spectator ions]

+ve ions are called cations, as they get attracted to cathode (-ve terminal), while -ve ions are called anions, as they get attracted to anode (+ve terminal),

Balancing number of atoms of both sides of eqn is needed, as no atoms can disappear or appear out of no where.

ex: CH4 (g) + H2O (g) => CO (g) + 3H2 (g) [Here we have to have 3 molecules of H2 gas on RHS to have same number of H atoms on LHS (total of 6 H atoms on both sides of the eqn]

Stoichiometry: Here we solve chemical eqn to find how many grams of compounds react to produce how many grams of final product. This can be done by first balancing the eqn, then finding out molar mass of molecules on the 2 sides, and then covert it to grams. This is pure Maths addition/subtraction.

Oxidation Reduction (redox reaction):  An oxidation–reduction is a reaction that involves the transfer of electrons between chemical species (the atoms, ions, or molecules involved in the reaction). During a redox reaction, some species undergo oxidation, or the loss of electrons, while others undergo reduction, or the gain of electrons.

Oxidation is process of something combining with oxygen. Since oxygen has 6 electrons in outermost shell (n=2), it needs 2 more electrons to complete it's shell, which requires those electrons coming from the other compound, so oxidation is defined as loss of electrons (more +ve charge, gain of charge). Oxygen itself gains electrons, and that is defined as reduction (more -ve charge, loss of charge).

For example, consider the reaction between iron and oxygen to form rust:

4Fe(s) + 3O2 (g) => 2Fe2O3 (s) (rusting of iron)

Fe has 2 electrons in outermost 4s shell, so it loses 2 electrons to for Fe2+. But more stable form is Fe3+ where it loses an extra electron from 3d shell to form half filled 3d shell (from 3d6 to 3d5 ). So Fe2O3 is a chain like structure with 2 oxygen on the end attaching to 2 Fe atoms, while oxygen in the center forms 1 bond with each Fe atom, forming O2- for all 3 O atoms, and Fe loses 3 electrons for each Fe atom to form Fe3+. Fe is oxidized, while O is reduced.

An atom’s oxidation number (or oxidation state) is the imaginary charge that the atom would have if all of the bonds to the atom were completely ionic. Oxidation involves an increase in oxidation number, while reduction involves a decrease in oxidation number.

The sum of the oxidation numbers for all atoms in a neutral compound is equal to zero, while the sum for all atoms in a polyatomic ion is equal to the charge on the ion. Look in KA for examples on Oxidation number.

Half reaction method: To balance a redox equation using the half-reaction method, the equation is first divided into two half-reactions, one representing oxidation and one representing reduction, and then both are balanced independently and then combined in the end.

Equilibrium: In chemical eqn, we have reactants and products. At time=0, we only have reactants, and as time progresses, products are formed, and finally both reactants and products are in equilibrium at some time=T. We denote this by writing eqn with arrow pointing in both forward and backward dir. At equilbrium, the conc of reactants and products remain constant. Reactants are still changing into products and products are still changing into reactants, but it happens at the same rate, so their conc don't change at equilibrium. 

Let's say reaction is aA + bB ⇔ cC + dD where A and B are reactants and C and D are products. a, b, c, d refer to number of molecules of each of these needed to form balanced eqn.

Molar conc: [A] (within square brackets) refers to molar conc of chemical A in moles/liter, written as M. So, 0.5M implies 0.5moles per liter. 1 liter of water weighs 1000g, 1 mole of water molecules weigh 18g, so molar conc of water is = 1000/18=55.5moles/liter = 55.5M.

Forward rate of reaction (R) = Kf * [A]^a * [B]^b

Forward rate of reaction (R) = Kr * [C]^c * [D]^d

We can plot graph of rate vs time,

  • At start of reaction, forward reaction rate R keeps decreasing with time as conc of reactants dec, while backward reaction rate R keeps increasing with time as conc of products inc.In this scenario, reactants get converted into products.
  • At equilibrium, these 2 rates become equal. 
  • If it so happens that rate of backward reaction is greater than rate of forward reaction, then products get converted back into reactants. 

Equilibrium Constant (Kc): We add subscript c to indicate it's constant for concentration. 

Kc = [A]^a * [B]^b / ([C]^c * [D]^d) => Here [A], [B], [C], [D] refer to equilibrium conc in M. The unit of Kc is either dimensionless or has M raised to the power of something. For only gases involved in reactions, it's more convenient to measure their partial pressure to determine their conc, so, we define Kc = [Pc]^c * [Pd]^d / ( [Pa]^a * [Pb]^b) where P reps partial pressure of each gas.

Kc is usually specified at 25C (room temp), as Kc  varies with temp (Kc generally dec with inc temp for exothermic reaction, and inc with inc temp for endothermic reaction). Kc  is defined for a given reaction written that particular way. If we change how we write the reaction by doubling the number of molecules on each side of eqn, then Kc for that new reaction will be sqaure of the original one. If we reverse the eqn, then new Kc  will be reciprocal of original value. If we get a reaction by adding up 2 reactions, then new Kc  will be product of the individual Kc  of ach reaction.

ex: HF (aq) ⇔ H+ (aq) + F- (aq)  For this reaction, Kc = 6.8*10^-4. If we multiply the reaction by 2, i.e 2HF (aq) ⇔ 2H+ (aq) + 2F- (aq) , then new Kc = (6.8*10^-4)^2 as all products and reactants get squared now. If we reverse the reaction to H+ (aq) + F- (aq) ⇔ HF (aq), then for this reaction, new Kc will be inverse of original one, i.e new Kc = 1/(6.8*10^-4).

However, if reaction involves a mix of solid and gas, then it's a heterogeneous reaction (in contrast to homogeneous reaction where all products and reactants are in same phase). We define this as heterogeneous Equilibrium Constant, and in calculating this, we leave out pure solids and pure liquid in heterogeneous equilibrium constant since their conc don't change, so it doesn't help having this in the equilibrium constant defn.

Magnitude of Kc says how many products and reactants we have at equilibrium.

  • Kc < 1 => There are more reactants than products at equilibrium. If Kc << 1, this reaction barely proceeds in forward dir.
  • Kc > 1 => There are more products than reactants at equilibrium. If Kc >> 1, this reaction proceeds to completion.

Reaction Constant (Qc): Another term Qc is defined which is the Reaction Constant at any time, where the conc are instantaneous conc of reactants and products at that time. Term Qp is similarly defined for gases when rep in terms of partial pressures. 

  • If Qc < Kc  , that implies that there are more reactants than products, hence reaction is proceeding forward (to the right)
  • If Qc = Kc  , that implies that reaction is at equilibrium
  • If Qc > Kc  , that implies that there are more products than reactants, hence reaction is proceeding backward (to the left)

ICE table: An ICE table is made for any reaction, where I is initial partial pressure of all reactants/products, C=change in partial pressure and E is the equilibrium partial pressure. This ICE table can also be made for conc (instead of pressures).

To find E, we put I=initial Pressure, C=x for reactant (and -x for products, assuming coeff of 1 for both products and reactants), and then take E=I+C, and then solve for x by putting in Kc  to find final equilibrium pressures.

Le Chatelier's Principle: When stress is applied to a reaction at equilibrium, net reaction moves in a direction that relieves the stress.

  • Concentration: Changing conc of reactants and products is one way to change stress at equilibrium. If we have a reaction where gas A is changing into gas B, then inc conc of Gas A, will cause the reac to go in dir where gas A will dec, meaning reaction moves forward. This can also be found out by calc Qc which is less than Kc , hence reaction moves forward
  • Volume: Changing vol of reactants and products is another way to change stress at equilibrium. Let's say we have a reaction of solid turning into another solid and gas in a container, and we dec the vol of container. Then pressure of gas will inc, and the reaction will try to dec pressure of gas, making reaction go to the left to dec gas pressure and inc amount of reactant.
    • NOTE: adding any inert gas at equil doesn't change the reaction at equil, since inert gas is not participating in the reaction, so Qc remains the same and equal to Kc 
  • Temperature: Changing Temp is another stress. This is the only one where we Kc for the reaction changes.
    • For an exothermic reaction (ΔH < 0), heat is released. Let's say we inc the temp, which inc heat. If heat is treated as a product, then reaction will try to go in a dir that moves it to the left so that heat is decreased. If we dec the temp, then reverse happens (reaction moves to right). We can also evaluate this by calc Kc , which inc as temp dec, so Qc < Kc , implying reaction will move to right.
    • For an endothermic reaction (ΔH > 0), heat is consumed. Reverse happens over here, as compared to exothermic reaction. Let's say we inc the temp, which inc heat. If heat is treated as a reactant, then reaction will try to go in a dir that moves it to the right so that heat is decreased. If we dec the temp, then reverse happens (reaction moves to left). We can also evaluate this by calc Kc , which inc as temp inc, so Qc < Kc , implying reaction will move to right.
    • Catalyst: One way to inc rate of reaction for exothermic reaction is to increase temp. However that changes Kc which changes the amount of products. If we don't want to change the amount of products, we can add a catalyst, which allows the reaction to reach equilibrium faster. Catalyst speeds up both the forward and backward reaction by same amount, hence Kc remains the same.

 


 

Dissociation of Water molecules: 

When we talk of water, we treat it as having all it's molecules present as H2O. In reality, some of it's molecules dissociate into H+ and OH-. The H+ combines with a lone H2O molecule to form hydronium ion, H3O+.  About 6 in every 100 million (6 in 108) water molecules undergo the following reaction: 

  H2O (l) +  H2O (l) <=>  H3O+ (aq) + OH- (aq) Here l refers to liquid while aq refers to aqueous soln. The extra proton forms a covalent bond with 1 of the lone pair electron in oxygen. The reaction is bidir.

Dissociation of water => https://chem.libretexts.org/Bookshelves/Introductory_Chemistry/Introduction_to_General_Chemistry_(Malik)/06%3A_Acids_and_bases/6.05%3A_Dissociation_of_water

This process is called the autoionization of water and occurs in every sample of water, whether it is pure or part of a solution. This ionization of water gives rise to conductivity of water, albeit very low.

We can find the equilibrium.constant 

 

Conductivity of water: 

Let's take water as a solution in our standard cell to measure it's conductivity: electrodes of area A=1 cm2A=1 cm^2, gap d=1 cmd=1 cm, applied voltage V=1 VV=1 V, measured current I=5.5×10−8 AI=5.5×10^-8A (the 55 nA). It's extremely low current. Using R=V/I, we get R=1.8*10^7 ohms. So, resistivity ρ = R*L/A = 1.8*10^7 Ω-cm. So, resistivity is very high (almost insulator) as metals have conductivity in range of 10^-7 Ω-cm (10^14 times higher conductivity in metals).

The ionic conductivity of water arises from conductivity of  H+ and OH-. We saw in "passive elements" section, that for any conducting medium, σ = -q*n*µ = n*q2*t/m, where n=number of electrons crossing per unit area, q=charge and µ= electron mobility. If there are other carriers besides electrons, then they all have to be added, i.e in semiC, we have holes (p) too, so σ = - (q*n*µn + q*n*µp) . For solutions, we have similar mobility concept for ions, as the free ions in a solution are what become carriers (as the ions are the ones that move in response to electric field. it's not electrons or holes). 

Electrolyte (ion) conductivity in a solution (κ similar to σ in metals/SemiC) = ∑ (zi * F) * ui * Ci = ∑  λi * Ci , where λi= (zi * F) * ui = molar conductivity of ions per mole (charge_per_ion*Faraday*mobility_of_ion) and Ci= ion concentration (mole/cm^3). 

Faraday is defined as total charge (in Coulombs) on 1 mole of electrons = 1.6*10^-19 C * 6.02*10^23 e/mole = 96,500 C. So, (zi*F)= total charge on 1 mole of ions, which is equiv to "q" in our above classic resistivity eqn. This multiplied with mobility gives conductivity per mole of ions. When we multiply it with conc (mol/cm^3), we get total conductivity κ (in units 1/(Ω-cm) = Siemens/cm (S/cm) or in per volume unit of solution) = ∑  q*n*µ, where q=zi*F, n=Ci , µ=ui. 

However, in solutions, talking about conductivity in units of Ω-cm doesn't make much sense, because it will vary based on concentration of the solution. In metals, that was not a problem, since conc of electrons is fixed in metals. However for electrolytes, the same salt solution will have different conductivity based on conc of salt in water (i.e how many moles of salt are present per cm^3 of solution). So, we use Molar conductivity per mole λi as a more common unit to define conductivity of solutions, and then multiply it by conc (moles per unit volume) to get ionic conductivity. λi = (zi*F)*ui = charge*mobility = C*cm^2/(V-s)/mole = S.cm^2/mol (units obtained by dividing σ (S/cm) by conc (mol/cm^3)).

For most questions in electrolytic conductivity, you will see molar conductivity λi used for conductivity of solutions. We just need to multiply it by conc to get real conductivity, which is in same units as metal conductivity (S/cm). 

 

Acid and Base: 

Dissociation constant:

 

Definition of Acid/Base:

Bronsted and Lowry defined acid and base based on this. They defined acid as anything that is a proton/H+ donor. Coversely base was defined as anything that is a proton/H+ acceptor. 

ex: HCl (aq) + H2O (l) → Cl- + H3O+ Here HCl donated a proton (H+), so it's a acid, and H2O accepted a proton, so it's a base. The product formed from the acid after donating a proton is called "conjugate base" of the acid. Similarly the product formed from the base after accepting a proton is called "conjugate acid" of the base. 

The above eqn are generally rep by arrow in both direction, indicating the forward and backward reaction are both taking place at the same time, and it's a reaction that is in equilibrium.

Acid: donates H+. Product after donating becomes a conjugate base. It's called conjugate base, as it can accept H+. 

Base: accepts H+ . Product after accepting becomes a conjugate acid. It's called conjugate acid, as it can donate H+. 

Titration: It's a process for determining the conc of a acid/base in a solution. We find the unknown molar conc of a solution (i.e acid) by mixing it with known molar conc of another slution (i.e base) and note where does the resulting solution go neutral.

 


 

 

Mag 7 (Magnificent 7)

This is a term referred to top 7 companies in USA, which are in tech and highly successful. They have dominated the total stock market cap, and have provided higher return than S&P500. These are the 7 stocks => Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and Tesla. 

ETF: MAGS (Roundhill Magnificent Seven ETF) is the top ETF with 0.29% expense ratio and $3B in AUM to track Mag 7. It was launched on 04/11/23 and initially traded as ticker "BIGT". It has provided 100% return in 3 yrs since it's launch, excluding dividend.

We discuss the 7 stocks to see how worthy of investment are they:

Apple: 

 

 

IT BPO Companies

BPO is Business Process Outsourcing. It involves outsourcing services such as customer service, technical support, and back-office operations to other countries where cost of operation is lower. IT BPO refers specifically to IT service that is outsourced to other companies. These companies are more efficient in providing entire IT services for other companies. IT works out well for other companies too, as they don't want to have their own IT department to take care of billing, paychecks, etc. Total Market size of IT BPO is $450B as of 2026, and growing at 5%-8% a yr (estimates vary widely). I assume that all markets grow at same rate as what US GDP grows by, which is 5%/yr. USA is the largest market by revenue accounting for 30%-40%. India is the largest provider of IT BPO services, as is evident by the fact that most IT companies are Indian Co, or are situated in India. Besides India, other countries as Philippines, Vietnam, Indonesia are also emerging players. 

Global IT spending is $5.5T as of 2025, of which $1.7T is spent on IT services. BPO part of these IT services seems to be only $0.5T.